| 基于迁移学习的乳腺肿瘤超声图像智能分类诊断 |
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| 引用本文： | 吴英,罗良平,许波,黄君,赵璐瑜.基于迁移学习的乳腺肿瘤超声图像智能分类诊断[J].中国医学影像技术,2019,35(3):357-361. |
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| 作者姓名： | 吴英 罗良平 许波 黄君 赵璐瑜 |
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| 作者单位： | 暨南大学附属第一医院医学影像中心, 广东 广州 510630,暨南大学附属第一医院医学影像中心, 广东 广州 510630,广东财经大学信息学院, 广东 广州 510630,暨南大学附属第一医院医学影像中心, 广东 广州 510630,暨南大学附属第一医院医学影像中心, 广东 广州 510630 |
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| 基金项目： | 国家自然科学基金面上项目（81771973）。 |
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| 摘 要： | 目的 探讨迁移学习方法对乳腺良恶性肿瘤超声图像分类的价值。方法 回顾性分析经病理证实的447例乳腺肿瘤的超声声像图，采用主成分分析法对原始图像进行分析提取；在Matlab 7.0软件中编程实现迁移学习，将量化的图像特征作为输入数据，利用迁移学习对乳腺良恶性肿瘤进行智能分类。结果 乳腺恶性肿瘤的边缘粗糙度、坚固度、邻域灰度差矩阵粗糙度、肿瘤后方与周围区域回声差异及水平方向高频分量和垂直方向低频分量的直方图能量均明显高于良性肿瘤（P均<0.05）。超声和迁移学习方法诊断乳腺恶性肿瘤的敏感度分别为96.21%（127/132）和96.04%（97/101），特异度为66.35%（209/315）和98.49%（196/199），准确率为75.17%（336/447）和97.67%（293/300）。结论 超声图像特征定量化可为识别良恶性乳腺肿瘤提供客观的量化参数；迁移学习可有效对乳腺良恶性肿瘤的声像图进行分类。
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| 关 键 词： | 乳腺肿瘤 超声检查 迁移学习 特征提取 |
| 收稿时间： | 2018/7/8 0:00:00 |
| 修稿时间： | 2018/11/29 0:00:00 |
Classification and diagnosis of ultrasound images with breast tumors based on transfer learning |
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| WU Ying,LUO Liangping,XU Bo,HUANG Jun and ZHAO Luyu.Classification and diagnosis of ultrasound images with breast tumors based on transfer learning[J].Chinese Journal of Medical Imaging Technology,2019,35(3):357-361. |
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| Authors: | WU Ying LUO Liangping XU Bo HUANG Jun and ZHAO Luyu |
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| Institution: | Department of Medical Imaging Center, the First Affiliated Hospital of Jinan University, Guangzhou 510630, China,Department of Medical Imaging Center, the First Affiliated Hospital of Jinan University, Guangzhou 510630, China,School of Information, Guangdong University of Finance and Economics, Guangzhou 510630, China,Department of Medical Imaging Center, the First Affiliated Hospital of Jinan University, Guangzhou 510630, China and Department of Medical Imaging Center, the First Affiliated Hospital of Jinan University, Guangzhou 510630, China |
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| Abstract: | Objective To investigate the value of transfer learning methods in classification of ultrasound images of benign and malignant breast tumors. Methods Ultrasonic features of histopathologically proved breast tumors in 447 patients were retrospectively analyzed. The features of original images were extracted using the method of principal component analysis. Matlab 7.0 software was used for achieving transfer learning method. Finally, the quantitative image characteristics were inputted into the program in order to use new methods of transfer learning for identifying the benign and malignant breast tumors. Results The quantitative parameters of ultrasound images with malignant breast tumors, such as edge roughness, firmness, neighborhood gray-tone difference matrix roughness, echo difference between the posterior and peripheral areas of the masses, and the horizontal high-frequency and vertical low-frequency components-histogram energy were significantly higher than those of the benign breast tumors (all P<0.05). The sensitivity, specificity, the accuracy of the ultrasound and transfer learning method in diagnosis of malignant breast tumors was 96.21% (127/132) and 96.04% (97/101), 66.35% (209/315) and 98.49% (196/199), 75.17% (336/447) and 97.67% (293/300), respectively. Conclusion Quantitative ultrasonic features can provide objective quantitative parameters for identification of benign and malignant breast tumors. Transfer learning methods can effectively classify ultrasound images with benign and malignant breast tumors. |
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| Keywords: | breast neoplasms ultrasonography transfer learning feature extraction |
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